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Navabi, N., Moghaddasi, M., Gangi, N. (2021). Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin). , 13(1), 1-12. doi: 10.22092/ijwmse.2020.126860.1684
Naghem Navabi; Mahnoosh Moghaddasi; Naser Gangi. "Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin)". , 13, 1, 2021, 1-12. doi: 10.22092/ijwmse.2020.126860.1684
Navabi, N., Moghaddasi, M., Gangi, N. (2021). 'Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin)', , 13(1), pp. 1-12. doi: 10.22092/ijwmse.2020.126860.1684
Navabi, N., Moghaddasi, M., Gangi, N. Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin). , 2021; 13(1): 1-12. doi: 10.22092/ijwmse.2020.126860.1684

Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin)

مهندسی و مدیریت آبخیز
Article 1, Volume 13, Issue 1, April 2021, Pages 1-12    PDF (1.43 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2020.126860.1684
Authors
Naghem Navabi1; Mahnoosh Moghaddasi* 2; Naser Gangi3
1Water Resources-Aarak university
2Water Engineering- Faculty of Agricultiral- Arak university- Arak -Iran
3water resources-Arak Uiversity
Abstract
Drought is a natural phenomenon that occurs in almost all parts of the world. The effects of this crawling phenomenon are more pronounced in arid and semi-arid areas due to their annual rainfall. In contrast to traditional methods based on meteorological stations observations that focus more on weather drought, the use of remote sensing and satellite imagery as a useful tool for monitoring agricultural drought has been considered. In the present study, the aim of comparing and assessing agricultural drought monitoring in Urmia Lake basin using VCI, VHI, TCI vegetation cover indices during the years 2000 to 2011 is using Madison. For this purpose, the NDVI index was first calculated from the images of Madis during June, July, August and September. Then, by comparing the mean of this index during these months, Shahrivar was selected with the maximum value as the month of the indicator. With regard to the minimum and maximum NDVI index in the months of September 2003 and 2008, VCI, VHI, TCI dash mapping maps were prepared. In order to evaluate the performance of agricultural drought indices, correlation coefficients were calculated for VCI, VHI and TCI profiles with SPI Meteorological Index. The results showed that the remote sensing index had a good accuracy in estimating the spatial and temporal dispersion of agricultural drought, so that the correlation coefficient between the VHI and SPI index was 0.86, which indicates that the index is consistent with the SPI meteorological index.
Keywords
"Agricultural drought"; "Remote Sensing"; "Standardized Precipitation Index"; "Vegetarian Index"
References
  1. Dabrowska-Zielinska, K., F. Kogan, A. Ciolkosz, M. Gruszczynska and W. Kowalik. 2002. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices. International Journal of Remote Sensing, 23(6): 1109-1123.
  2. Damavandi, A.A., M. Rahimi, M.R. Yazdani and A.A. Noroozi. 2016. Spatial monitoring of agricultural drought through time series data of NDVI and LST indices and Modis data in Lake Urmia Catchment. Scientific Research Quarterly of Geographical Data (SEPEHR), 25(99): 25-48 (in Persian).
  3. Gidey, E., O. Dikinya, R. Sebego, E. Segosebe and A. Zenebe. 2018. Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environmental Systems Research, 7(1): 13-29.
  4. Heim, R.R. 2002. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8): 1149-1166.
  5. Kogan, F.N. 1997. Global drought watch from space. Bulletin of the American Meteorological Society, 78(4): 621-636.
  6. Kogan, F.N. 2005. NOAA/AVHRR satellite data based indices for monitoring agricultural droughts. New York, Oxford University Press, 88 pages.
  7. McKee, T.B., N.J. Doesken and J. Kleist. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17(22): 179-183.
  8. Orhan, O., S. Ekercin and F. Dadaser-Celik. 2014. Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin area, Turkey. The Scientific World Journal, 2014(1): 14-29.
  9. Son, N.T., C.F. Chen, C.R. Chen, L.Y. Chang and V.Q. Minh. 2012. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18: 417-427.
  10. Owrangi, M.A., J. Adamowski, M. Rahnemaei, A. Mohammadzadeh and R.A. Sharifan. 2011. Drought monitoring methodology based on AVHRR images and SPOT vegetation maps. Journal of Water Resource and Protection, 3(5): 325-346.
  11. Rostami, A., M. Bzane and M. Raini. 2016. Spatial and temporal monitoring of agricultural drought using Modis imagery and remote sensing technology. Journal of Soil and Water Science, 27: 213-226 (in Persian).
  12. Tonini, F., G.J. Lasinio and H.H. Hochmair. 2012. Mapping return levels of absolute NDVI variations for the assessment of drought risk in Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 18: 564-572.
  13. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
  14. Yıldırım, T. and Ş. Aşık. 2018. Index-based assessment of agricultural drought using remote sensing in the semi-arid region of Western Turkey. Journal of Agricultural Sciences, 24(4) : 510-516.
  15. Zhang, J. 2004. Risk assessment of drought disaster in the maize-growing region of Songliao Plain, China. Agriculture, Ecosystems and Environment, 102(2): 133-153.
  16. Zhang, N., Y. Hong, Q. Qin and L. Liu. 2013. VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. International Journal of Remote Sensing, 34(13): 4585-4609.
  17. Zhang, X., M.A. Friedl, C.B. Schaaf, A.H. Strahler, J.C. Hodges, F. Gao and A. Huete. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475.
  18. Zamanian, M.T., M.B. Behyar, A. Hosseini Karimi and M. Vazife Dust. 2016. Monitoring and analysis of drought using NOAA-AVHRR satellite sensing products 2012. Journal of Climatological Research, 9(3): 33-54.
  19. Zambrano, F., M. Lillo-Saavedra, K. Verbist and O. Lagos. 2016. Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sensing, 8(6): 530-549.
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